Merge pull request #360 from CartoDB/doc-multi-version

Doc multi version

docs/guides/01-overview.md

@@ -1,88 +1,8 @@
 ## Overview
 
-For Enterprise account plans, the [Data Observatory](https://carto.com/data) provides access to a searchable catalog of advanced location data, such as census block, population segments, boundaries and so on. A set of SQL functions allow you to augment your own data and broaden your analysis by discovering boundaries and measures of data from this catalog.
+Quick reference guides for learning how to use the Data Observatory features.
 
-This section describes the Data Observatory functions and the type of data that it returns.
+- [Data discovery guide](https://carto.com/developers/cartoframes/guides/Data-discovery/)
+- [Data enrichment guide](https://carto.com/developers/cartoframes/guides/Data-enrichment/)
 
-### Functions Overview
-
-There are several functions for accessing different categories of data into your visualizations. You can discover and retrieve data by requesting OBS functions from the Data Observatory. These Data Observatory functions are designed for specific, targeted methods of data analysis. The response for these functions are classified into two primary types of data results; measures and boundaries.
-
-- Boundaries are the geospatial boundaries you need to map or aggregate your data. Examples include Country Borders, Zip Code Tabulation Areas, and Counties
-
-- Measures are the various dimensions of information that CARTO can tell you about a place. Examples include, Population, Household Income, and Median Age
-
-Depending on the OBS function, you will get one, or both, types of data in your result. See [Measures and Boundary Data](#measures-and-boundary-results) for details about available data.
-
-#### Measures Functions
-
-Use location-based measures to analyze your data by accessing population and industry measurements at point locations, or within a region or polygon. These include variables for demographic, economic, and other types of information.
-
-- See [Measures Functions]({{ site.dataobservatory_docs }}/reference/#measures-functions) for specific OBS functions
-- Returns Measures data results
-
-#### Boundary Functions
-
-Use global boundaries to analyze your data by accessing multi-scaled geometries for visualizations. Examples include US Block Groups and Census Tracts. These enable you to aggregate your data into geometric polygons. You can also use your own data to query specific boundaries.
-
-- See [Boundary Functions]({{ site.dataobservatory_docs }}/reference/#boundary-functions) for specific OBS functions
-- Returns Boundary data results
-
-#### Discovery Functions
-
-Discovery Functions provide easier ways for you to find Measures and Boundaries of interest in the Data Observatory. The Discovery functions allow you to perform targeted searches for Measures, or use your own data to discover what is available at a given location. As this is a **retrieval tool** of the Data Observatory, the query results do not change your table. The response back displays one or more identifiers as matches to your search criteria. Each unique identifier can _then_ be used as part of other OBS functions to access any of the other Data Observatory functions.
-
-- See [Discovery Functions]({{ site.dataobservatory_docs }}/reference/#discovery-functions) for specific OBS functions
-- Returns Boundary or Measures matches for your data
-
-### Measures and Boundary Results
-
-The response from the Data Observatory functions are classified as either Measures or Boundary. Depending on your OBS function, you will get one, or both, types of data in your result.
-
-#### Measures Data
-
-Measures provide details about local populations, markets, industries and other dimensions. You can search for available Measures using the Discovery functions, or by viewing the Data Catalog. Measures can be requested for Point locations, or can be summarized for Polygons (regions). In general, Point location requests will return raw aggregate values (e.g. Median Rent), or will provide amounts per square kilometer (e.g. Population). The total square kilometers of the area searched will be returned, allowing you to get raw counts, if needed. Alternatively, if you search over a polygon, raw counts will be returned.
-
-The following table indicates where Measures data results are available. Measures can include raw measures and when indicated, can provide geometries.
-
-Data Category | Examples | Type of Data Response | Availability
---- | ---
-Housing | Vacant Housing Units, Median Rent, Units for Sale, Mortgage Count | Point measurement, Area measurement, With Geo Border | United States
-Income | Median Household Income, Gini Index | Point measurement, Area measurement, With Geo Border | United States
-Education | Students Enrolled in School, Population Completed H.S | Point measurement, Area measurement, With Geo Border | United States
-Languages | Speaks Spanish at Home, Speaks only English at Home | Point measurement, Area measurement, With Geo Border | United States
-Employment | Workers over the Age of 16 | Point measurement, Area measurement, With Geo Border | United States
-Jobs and Workforce | Origin-Destination of Workforce, Job Wages by job type | Point measurement, Area measurement, With Geo Border | United States
-Transportation | Commuters by Public Transportation, Work at Home | Point measurement, Area measurement, With Geo Border | United States
-Race, Age and Gender | Asian Population, Median Age, Job wages by race | Point measurement, Area measurement, With Geo Border | United States, Spain
-Population | Population per Square Kilometer | Point measurement, Area measurement | United States, Spain
-
-#### Boundary Data
-
-The following table indicates where Boundary data results are available.
-
-Boundary Name | Availability
---- | ---
-Countries | Global
-First-level administrative subdivisions | Global
-Second-level administrative subdivisions | United States
-Zip Code Tabulation Areas (ZCTA) | United States
-Congressional Districts | United States
-Digital Marketing Areas | United States
-Census Public Use Microdata Areas | United States
-Census Tracts |United States
-Census Block Groups | United States
-US Census Blocks | United States
-Disputed Areas | Global
-Marine Area | Global
-Oceans | Global
-Continents | Global
-Timezones | Global
-
-##### Water Clipping Levels
-
-Many geometries come with various degrees of water accuracy (how closely they follow features such as coastlines). Water clipping refers to how the level of accuracy is returned by the Data Observatory. Data results can either include no clip (no water areas are clipped in the geometry), or high clip (coastlines and inland waterways are clipped out of the final geometry). For example, US Census data might only show coastlines as a straight border line, and not as an inland water area. To find out which levels of water clipping are available for Boundary layers, refer to the [Data Catalog](https://cartodb.github.io/bigmetadata/index.html).
-
-**Note:** While high clip water levels may be better for some kinds of maps and analysis, this type of data consumes more account storage space and may be subject to quota limitations.
-
-For details about how to access any of this data, see [Accessing the Data Observatory]({{ site.dataobservatory_docs }}/guides/accesssing-the-data-observatory/).
+Play with [real examples](https://carto.com/developers/cartoframes/examples/#example-data-observatory).

docs/reference/01-introduction.md

@@ -1,5 +1,5 @@
 ## Introduction
 
-The Data Observatory, available for Enterprise accounts, provides access to a catalog of analyzed data methods, and enables you to apply the results to your own datasets.
+Browse the interactive API documentation to search for specific Data Observatory methods, arguments, and sample code that can be used to build your applications.
 
-The contents described in this document are subject to CARTO's [Terms of Service](https://carto.com/legal/)
+[Check the reference](https://carto.com/developers/cartoframes/reference/#heading-Data-Observatory).

docs/v1/guides/01-overview.md

@@ -0,0 +1,88 @@
+## Overview
+
+For Enterprise account plans, the [Data Observatory](https://carto.com/data) provides access to a searchable catalog of advanced location data, such as census block, population segments, boundaries and so on. A set of SQL functions allow you to augment your own data and broaden your analysis by discovering boundaries and measures of data from this catalog.
+
+This section describes the Data Observatory functions and the type of data that it returns.
+
+### Functions Overview
+
+There are several functions for accessing different categories of data into your visualizations. You can discover and retrieve data by requesting OBS functions from the Data Observatory. These Data Observatory functions are designed for specific, targeted methods of data analysis. The response for these functions are classified into two primary types of data results; measures and boundaries.
+
+- Boundaries are the geospatial boundaries you need to map or aggregate your data. Examples include Country Borders, Zip Code Tabulation Areas, and Counties
+
+- Measures are the various dimensions of information that CARTO can tell you about a place. Examples include, Population, Household Income, and Median Age
+
+Depending on the OBS function, you will get one, or both, types of data in your result. See [Measures and Boundary Data](#measures-and-boundary-results) for details about available data.
+
+#### Measures Functions
+
+Use location-based measures to analyze your data by accessing population and industry measurements at point locations, or within a region or polygon. These include variables for demographic, economic, and other types of information.
+
+- See [Measures Functions]({{ site.dataobservatory_docs }}/reference/#measures-functions) for specific OBS functions
+- Returns Measures data results
+
+#### Boundary Functions
+
+Use global boundaries to analyze your data by accessing multi-scaled geometries for visualizations. Examples include US Block Groups and Census Tracts. These enable you to aggregate your data into geometric polygons. You can also use your own data to query specific boundaries.
+
+- See [Boundary Functions]({{ site.dataobservatory_docs }}/reference/#boundary-functions) for specific OBS functions
+- Returns Boundary data results
+
+#### Discovery Functions
+
+Discovery Functions provide easier ways for you to find Measures and Boundaries of interest in the Data Observatory. The Discovery functions allow you to perform targeted searches for Measures, or use your own data to discover what is available at a given location. As this is a **retrieval tool** of the Data Observatory, the query results do not change your table. The response back displays one or more identifiers as matches to your search criteria. Each unique identifier can _then_ be used as part of other OBS functions to access any of the other Data Observatory functions.
+
+- See [Discovery Functions]({{ site.dataobservatory_docs }}/reference/#discovery-functions) for specific OBS functions
+- Returns Boundary or Measures matches for your data
+
+### Measures and Boundary Results
+
+The response from the Data Observatory functions are classified as either Measures or Boundary. Depending on your OBS function, you will get one, or both, types of data in your result.
+
+#### Measures Data
+
+Measures provide details about local populations, markets, industries and other dimensions. You can search for available Measures using the Discovery functions, or by viewing the Data Catalog. Measures can be requested for Point locations, or can be summarized for Polygons (regions). In general, Point location requests will return raw aggregate values (e.g. Median Rent), or will provide amounts per square kilometer (e.g. Population). The total square kilometers of the area searched will be returned, allowing you to get raw counts, if needed. Alternatively, if you search over a polygon, raw counts will be returned.
+
+The following table indicates where Measures data results are available. Measures can include raw measures and when indicated, can provide geometries.
+
+Data Category | Examples | Type of Data Response | Availability
+--- | ---
+Housing | Vacant Housing Units, Median Rent, Units for Sale, Mortgage Count | Point measurement, Area measurement, With Geo Border | United States
+Income | Median Household Income, Gini Index | Point measurement, Area measurement, With Geo Border | United States
+Education | Students Enrolled in School, Population Completed H.S | Point measurement, Area measurement, With Geo Border | United States
+Languages | Speaks Spanish at Home, Speaks only English at Home | Point measurement, Area measurement, With Geo Border | United States
+Employment | Workers over the Age of 16 | Point measurement, Area measurement, With Geo Border | United States
+Jobs and Workforce | Origin-Destination of Workforce, Job Wages by job type | Point measurement, Area measurement, With Geo Border | United States
+Transportation | Commuters by Public Transportation, Work at Home | Point measurement, Area measurement, With Geo Border | United States
+Race, Age and Gender | Asian Population, Median Age, Job wages by race | Point measurement, Area measurement, With Geo Border | United States, Spain
+Population | Population per Square Kilometer | Point measurement, Area measurement | United States, Spain
+
+#### Boundary Data
+
+The following table indicates where Boundary data results are available.
+
+Boundary Name | Availability
+--- | ---
+Countries | Global
+First-level administrative subdivisions | Global
+Second-level administrative subdivisions | United States
+Zip Code Tabulation Areas (ZCTA) | United States
+Congressional Districts | United States
+Digital Marketing Areas | United States
+Census Public Use Microdata Areas | United States
+Census Tracts |United States
+Census Block Groups | United States
+US Census Blocks | United States
+Disputed Areas | Global
+Marine Area | Global
+Oceans | Global
+Continents | Global
+Timezones | Global
+
+##### Water Clipping Levels
+
+Many geometries come with various degrees of water accuracy (how closely they follow features such as coastlines). Water clipping refers to how the level of accuracy is returned by the Data Observatory. Data results can either include no clip (no water areas are clipped in the geometry), or high clip (coastlines and inland waterways are clipped out of the final geometry). For example, US Census data might only show coastlines as a straight border line, and not as an inland water area. To find out which levels of water clipping are available for Boundary layers, refer to the [Data Catalog](https://cartodb.github.io/bigmetadata/index.html).
+
+**Note:** While high clip water levels may be better for some kinds of maps and analysis, this type of data consumes more account storage space and may be subject to quota limitations.
+
+For details about how to access any of this data, see [Accessing the Data Observatory]({{ site.dataobservatory_docs }}/guides/accessing-the-data-observatory/).

docs/v1/reference/01-introduction.md

@@ -0,0 +1,5 @@
+## Introduction
+
+The Data Observatory, available for Enterprise accounts, provides access to a catalog of analyzed data methods, and enables you to apply the results to your own datasets.
+
+The contents described in this document are subject to CARTO's [Terms of Service](https://carto.com/legal/)